The graph above displays the sentiment predicted for 188,638 tweets that contain the keyword “Credit Suisse”, along with the daily stock price change (in %) of Credit Suisse shares. To predict the sentiment of a tweet, I use a Natural Language Processing model trained on financial texts (FinBERT, Araci (2019)).
In the plot, the sentiment measure represents the average sentiment per day of all collected tweets. The sentiment is then weighted by the number of tweets collected that day, divided by the total number of tweets. The measure is normalized (mean subtracted and divided by its standard deviation).1 Higher values correspond to a more positive sentiment, whereas lower values represent a negative sentiment. For the daily change in stock prices, missing values over the weekend are linearly interpolated to improve the readability of the chart. The correlation between the two series is 0.43.
A few notes on the negative spikes in sentiment values in the graph and news media reporting around the same time:
On September 22nd, 2022, news emerged that Credit Suisse might consider splitting their investment bank (Reuters (2022a)) and that they were sounding out investors about a capital hike (Reuters (2022c)).
Around October 2nd, 2022, news emerged that Credit Suisse might be looking to raise capital. At the same time, the price of Credit Default Swaps increased strongly (CNBC (2022)).
Around October 27th, 2022, Credit Suisse confirmed raising capital, announced a job cut, and informed about their new strategy (Credit Suisse (2022), Reuters (2022b)).
On November 1st, 2022, S&P downgraded Credit Suisse’s credit rating to BBB- (Wall Street Journal (2022)).
On March 15th, 2023, the chairman of Saudi National Bank announced that the bank wouldn’t boost its share of the bank (Bloomberg (2022)).
For more information, there is a growing literature that investigates the links between social media on financial markets. See, for example, Goutte (2022), Broadstock and Zhang (2019), Antweiler and Frank (2004), Sprenger et al. (2014).
References and Sources
The mean and standard deviation are calculated without observations of March 2023.↩︎